AI Model Evaluation with Business Utility Metrics

AI model evaluation can overstate readiness when it measures average task performance but ignores variability across repeated runs. The article introduces Business Utility, a risk-adjusted metric that combines analytical quality with repeatability for LLM data-analysis agents. It explains how volatility, measured through the Coefficient of Variation and discounted as operational risk, can reveal models that look strong on average but remain unreliable for business use. Technology leaders should evaluate AI agents by predictable decision utility, not isolated successful outputs.